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Short-term trajectory planning using reinforcement learning within a neuromorphic control architecture
KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).ORCID iD: 0000-0001-5998-9640
2019 (English)In: ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN (i6doc.com) , 2019, p. 649-654Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we present a first step towards neuromorphic vehicle control. We propose a modular and hierarchical system architecture entirely implemented in a spiking neuron substrate, which allows for the adjustment of individual components through either supervised or reinforcement learning as well as future deployment on dedicated neuromorphic hardware. In a sample instantiation, we investigate automated training of a neuromorphic trajectory selection module using reinforcement learning to demonstrate the general feasibility of our approach. We evaluate our system using the open-source race car simulator TORCS.

Place, publisher, year, edition, pages
ESANN (i6doc.com) , 2019. p. 649-654
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-258186Scopus ID: 2-s2.0-85071285408ISBN: 9782875870650 (print)OAI: oai:DiVA.org:kth-258186DiVA, id: diva2:1356748
Conference
27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2019; Bruges; Belgium; 24 April 2019 through 26 April 2019
Note

QC 20191002

Available from: 2019-10-02 Created: 2019-10-02 Last updated: 2019-10-02Bibliographically approved

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Conradt, Jörg

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